Field of Invention
The present invention relates to a method for predicting a remaining useful life of a lithium battery based on a wavelet denoising and a relevance vector machine, and more particularly to a method which extracts raw data through a wavelet double denoising, and establishes a prediction model with a relevance vector machine based on the raw data extracted, for predicting a remaining useful life of a lithium battery.
Description of Related Arts
Lithium batteries are widely used in portable electronic devices, electric vehicles, military electronic systems, etc. Failure of the lithium batteries will cause device performance degradation, malfunction, slow response and other electronic failures. Therefore, it is very necessary to predict the remaining useful life of lithium batteries.
Generally, health of the lithium battery is indicated by capacity, and capacity data are obtained by measurement in continual charge-discharge cycles. During measurement, due to unavoidable presence of electromagnetic interference, measurement error, random load, and unpredictable physical or chemical behavior inside the lithium battery, lithium battery capacity measurement data obtained generally comprises various types of noise with different strengths, rendering it impossible to accurately predict the battery life.
Wavelet denoising is a newly-developed method for extracting the raw data. With wavelet decomposition and reconstruction, the raw data can be extracted while noise is eliminated or reduced. However, since the noise of the lithium battery capacity data has many types and different sizes, it is difficult to sufficiently remove the noise with the basic wavelet denoising algorithm. Relevance vector machine is a regression prediction algorithm based on Bayesian framework, which has a high computing speed, and is suitable for online testing. It has been proved that the prediction accuracy of the relevance vector machine is higher than commonly used algorithms such as support vector machine algorithm and neural networks. For relevance vector machine algorithm, width factor of kernel function has a great impact on the prediction accuracy, which is obtained by past experience, causing a low prediction accuracy of the remaining useful life of the lithium battery.